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A Comprehensive Care Management Program to Prevent Chronic Obstructive Pulmonary Disease Hospitalizations

2012· article· en· W2036326371 on OpenAlexaff
Vincent S. Fan, J. Michael Gaziano, Robert Lew, Jean Bourbeau, Sylvia Adams, Sarah Leatherman, Soe Soe Thwin, Grant D. Huang, Richard Robbins, Peruvemba Sriram, Amir Sharafkhaneh, M. Jeffery Mador, George A. Sarosi, Ralph J. Panos, Padmashri Rastogi, Todd H. Wagner, Steven A. Mazzuca, Colleen Shannon, Cindy Colling, Matthew H. Liang, James K. Stoller, Louis D. Fiore, Dennis E. Niewoehner

Bibliographic record

VenueAnnals of Internal Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsMcGill UniversityVeterans Affairs Canada
Fundersnot available
KeywordsMedicinePulmonary diseaseIntensive care medicineDisease managementMEDLINEDiseaseEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Improving a patient's ability to self-monitor and manage changes in chronic obstructive pulmonary disease (COPD) symptoms may improve outcomes. OBJECTIVE: To determine the efficacy of a comprehensive care management program (CCMP) in reducing the risk for COPD hospitalization. DESIGN: A randomized, controlled trial comparing CCMP with guideline-based usual care. (ClinicalTrials.gov registration number: NCT00395083) SETTING: 20 Veterans Affairs hospital-based outpatient clinics. PARTICIPANTS: Patients hospitalized for COPD in the past year. INTERVENTION: The CCMP included COPD education during 4 individual sessions and 1 group session, an action plan for identification and treatment of exacerbations, and scheduled proactive telephone calls for case management. Patients in both the intervention and usual care groups received a COPD informational booklet; their primary care providers received a copy of COPD guidelines and were advised to manage their patients according to these guidelines. Patients were randomly assigned, stratifying by site based on random, permuted blocks of variable size. MEASUREMENTS: The primary outcome was time to first COPD hospitalization. Staff blinded to study group performed telephone-based assessment of COPD exacerbations and hospitalizations, and all hospitalizations were blindly adjudicated. Secondary outcomes included non-COPD health care use, all-cause mortality, health-related quality of life, patient satisfaction, disease knowledge, and self-efficacy. RESULTS: Of the eligible patients, 209 were randomly assigned to the intervention group and 217 to the usual care group. Citing serious safety concerns, the data monitoring committee terminated the intervention before the trial's planned completion after 426 (44%) of the planned total of 960 patients were enrolled. Mean follow-up was 250 days. When the study was stopped, the 1-year cumulative incidence of COPD-related hospitalization was 27% in the intervention group and 24% in the usual care group (hazard ratio, 1.13 [95% CI, 0.70 to 1.80]; P= 0.62). There were 28 deaths from all causes in the intervention group versus 10 in the usual care group (hazard ratio, 3.00 [CI, 1.46 to 6.17]; P= 0.003). Cause could be assigned in 27 (71%) deaths. Deaths due to COPD accounted for the largest difference: 10 in the intervention group versus 3 in the usual care group (hazard ratio, 3.60 [CI, 0.99 to 13.08]; P= 0.053). LIMITATIONS: Available data could not fully explain the excess mortality in the intervention group. Ability to assess the quality of the educational sessions provided by the case managers was limited. CONCLUSION: A CCMP in patients with severe COPD had not decreased COPD-related hospitalizations when the trial was stopped prematurely. The CCMP was associated with unanticipated excess mortality, results that differ markedly from similar previous trials. A data monitoring committee should be considered in the design of clinical trials involving behavioral interventions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.368
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations340
Published2012
Admission routes1
Has abstractyes

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